INTRODUCTION
Restaurants across NYC started reopening their doors since June 22, as the city officially moved into phase two of reopening. After more than six months of the COVID-19 pandemic, restaurants were able to have visitors for indoor dining starting on September 30 at 25 % capacity. Being in NYC surrounded by multiple options where to go for a lunch break I decided to investigate the restaurant’s inspection results to make sure that it’s safe. My research question was to identify restaurants with the highest grades in the Kips Bay area and investigate this distribution among all the NYC neighborhoods.
INSPIRATION
The design of visualization was inspired by the “Visualizing New York City Restaurant Inspections using SAP Analytics Cloud” article where over 30,000 were analyzed based on the inspection results. I didn’t have the same kind of data because the names of the restaurants in the dataset I used were anonymized, but it gave me an idea of what kind of charts I can include into my dashboard (heatmaps, bar and pie charts) and how to make them easy to interpret for readers. My second source of inspiration was “The United States death rate from drug overdoses is up nearly 250% since 2000: here’s a state by state breakdown” article on Reddit. This visualization became a background for color voice in my dashboard. A combination of brick and beige colors were used in my visualization. For example, I used bright colors to show restaurants with higher scores and lighter tones for lower scored restaurants on the map. The ABCEats restaurant inspection results tool created for easier search for restaurants by letter grade, location or type of food served based on the dataset I used also inspired me with an idea to create a map with filtering options.
PROCESS & MATERIALS
The dataset for the analysis was found on the NYC OpenData website. The DOHMH New York City Restaurant Inspection Results dataset contained every sustained or not yet adjudicated violation citation from every full or special program inspection conducted up to three years prior to the most recent inspection for restaurants and college cafeterias in an active status in August 2020. When inspection results in more than one violation, values for associated fields are repeated for each additional violation record. The dataset included a total of 400065 rows and 26 features including location data, dates, multiple quantitative and categorical dimensions. The following fields of the dataset were used for the visualization:
Restaurants Grades – Map (Longitude, Latitude, Grade, Zip Code to create a map; Street and Grade for filtering the results);
Distribution of Cuisine Type – Heatmap (Cuisine Description, Number of Records);
The Distribution of Critical Flags by Neighborhood – Bar Chart (Boro, Number of records, Critical flag);
Number of Inspections by Neighborhood from 2015 to 2020 – Line Chart (Year, Boro, Number of Records);
Grades Distribution by Neighborhood – Horizontal Bar Chart (Grade, Boro, Number of Records)
Downloaded CSV dataset was cleaned using Open Refine and uploaded to Tableau Public (desktop version). Five charts were created, combined within one dashboard and exported to the Tableau Public online version.
RESULTS
As a result, the dashboard including five charts was developed. Visualized data helped me better understand this huge amount of data and answer the research question.
The results of the study showed that Manhattan counted the highest number of restaurants and more than half of all the restaurants (about 60%) have some sort of issue and marked with a critical flag. However, most of the restaurants got A (highest) score based on the inspection results. The most popular types of cuisine included American, Chinese, Mexican, Italian, etc.
The number of inspections had an increasing trend from 2015 to 2019, but we can see a significant increase in 2020 which might be related to the COVID-19 pandemic lockdown.
REFLECTION
NYC Open Data is a great source of timely and updated raw data which might be used to identify NYC problems or come up with potential insights for new initiatives, startups and small businesses. I found Tableau public as a powerful tool for exploring and visualizing the data without coding skills. Software turned out to be quite flexible in terms of design (fonts, colors, alignment). I’ve created multiple kinds of charts to explore the tool and approach the date from different angles.
Moving the study further it would be interesting to investigate types of cuisine by Grade and Number of Critical flag. Also, including more filters into the map might become better solution for finding the best place for dining.
SOURCES:
NYC OpenData. New York City Restaurant Inspection Results, https://data.cityofnewyork.us/Health/DOHMH-New-York-City-Restaurant-Inspection-Results/43nn-pn8j/data
Visualizing New York City Restaurant Inspections using SAP Analytics Cloud, https://www.sapanalytics.cloud/resources-visualizing-new-york-city-restaurant-inspections/
The United States death rate from drug overdoses is up nearly 250% since 2000: here’s a state by state breakdown, https://www.reddit.com/r/dataisbeautiful/comments/a42a68/the_united_states_death_rate_from_drug_overdoses/?utm_source=ifttt
The ABCEats restaurant inspection results tool, https://a816-health.nyc.gov/ABCEatsRestaurants/#/Search
NYC OpenData, https://opendata.cityofnewyork.us/
Open Refine, https://openrefine.org/
Tableau Public, https://public.tableau.com/en-us/s/